Rapid closed-loop control based on machine learning

ABSTRACT

A system for rapidly adapting production of a product based on classification of production data using a classifier trained on prior production data is provided. A production control system includes a learning system and an adaptive system. The learning system trains a production classifier to label or classify previously collected production data. The adaptive system receives production data in real time and classifies the production data in real time using the production classifier. If the classification indicates a problem with the manufacturing of the product, the adaptive system controls the manufacturing to rectify the problem by taking some corrective action. The production classifier is trained using bootstrap data and corresponding example data extracted from prior production data. Once the bootstrap data is labeled, the corresponding example data is automatically labeled for use as training data.

BACKGROUND

Current manufacturing techniques rely in large part on computers tocontrol the equipment (e.g., machines) used to manufacture products. Asan example, in a mass production line to assemble a product (e.g., anautomobile), computers control robotic arms that position components inplace, weld the components together, polish the welds, and so on. Thecomputers may be connected to many different types of sensors to ensurethat the product is being assembled to specification. For example, whenpositioning a component, a computer may determine that a component iscorrectly positioned based on data received from an optic sensor and,when the component is positioned correctly, control the robotic arm tostart welding. After the welding is complete, the computer may determinewhether the weld meets acceptable quality standards based on variousmeasurements received from sensors that, for example, measure theheight, width, and length of the weld or measure the temperature of thewelder during the welding process. If the weld does not meet acceptablequality standards, the computer may direct a conveyor to divert theproduct from the assembly line for corrective action and may alert atechnician to perform maintenance on the welder or robotic arm.

Manufacturing is currently evolving to meet the need to develop customproducts rapidly. For example, a company that is manufacturing a productmay need a component that is not readily available. The company may havethe component custom made at a shop that specializes in making customproducts layer by layer. The shop may employ additive manufacturingtechniques to build up custom products. To build a custom product, thespecifications (e.g., dimensions and materials) are supplied to acomputer that controls the repeated adding of raw material to build upthe custom product. Additive manufacturing techniques (sometime referredto as 3-D printing) include extrusion deposition, sintering of granularmaterial, lamination, photopolymerization, and so on.

FIG. 1 is a diagram that illustrates an additive manufacturing processbased on selective laser melting (“SLM”). During manufacturing of anobject, layers of fabrication powder are successively deposited on afabrication bed. After each layer is deposited, a laser selectivelymelts portions of the layer to additively build the object in an objectcontainer system layer-by-layer. An SLM system 100 includes a lasersystem 110 and an object fabrication system 120. The laser systemincludes a laser 111 and a scanner system 112 to control laser beam 113.The object fabrication system includes a powder delivery system 130 andan object container system 140. The powder delivery system includes apowder delivery piston 131 and a roller 132. The object container systemincludes a fabrication piston 141 and the object 142 that is beingfabricated. At the start of each layer, the powder delivery piston movesup to feed more powder, and the fabrication piston moves down to makeroom for more powder. The roller then rolls the powder over the top ofthe object container system where there is room to deposit the powder.The scanner system directs the laser to selectively melt portions of thenewly deposited powder. The specifications indicate what portions of thepowder are to be melted for each layer. This process is repeated foreach layer. After the last layer has been selectively melted, the object(i.e., the melted powder) can be removed from the powder that was notmelted.

During manufacturing processes, vast amounts of data are currently beingcollected and stored, and even more data could be collected and stored.For example, an SLM system may employ sensors to measure the temperatureduring melting, roughness and density of a layer, dimensions of a layer,and so on. This data can be used to determine whether a product meetsits manufacturing specifications. In addition, the data collected fromthe manufacture of multiple products can be used to make improvements inthe manufacturing, such as preventing certain types of recurring defect.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram that illustrates an additive manufacturing processbased on selective laser melting (“SLM”).

FIG. 2 is a diagram that illustrates an example relationship between asnapshot of a weld and a video of the welding process.

FIG. 3 is a diagram that illustrates an example relationship between afriction map and height measurements of a manufacturing process.

FIG. 4 is a block diagram that illustrates components of a productioncontrol system in some embodiments.

FIG. 5 is a flow diagram that illustrates the processing of a generateproduction classifier component in some embodiments.

FIG. 6 is a flow diagram that illustrates the processing of a generatebootstrap classifier component in some embodiments.

FIG. 7 is a flow diagram that illustrates an adapt production componentin some embodiments.

FIG. 8 is a flow diagram that illustrates processing of an analyzeproduction data component in some embodiments.

DETAILED DESCRIPTION

A method and system for rapidly adapting production of a product basedon classification of production data using a classifier trained on priorproduction data is provided. In some embodiments, a production controlsystem includes a learning system and an adaptive system. The learningsystem trains a production classifier to label or classify previouslycollected production data (e.g., as being acceptable or unacceptable).For example, if the production data is a video of a weld as a weldermoves across a surface, the production classifier may be trained toclassify a frame of a video based on weld characteristics indicatingquality of the weld. The weld characteristics may include perfect, abreak in the weld, a pit in the weld, a weakly sintered weld, a smallglob, a medium glob, and so on. The adaptive system receives productiondata in real time and classifies the production data in real time usingthe production classifier. If the classification indicates a problemwith the manufacturing of the product, the adaptive system controls themanufacturing to rectify the problem by taking some corrective action.For example, if successive frames of a video of a weld during thewelding process indicate a break, then the adaptive system may directthat the welder move back and re-weld the portion with the break.Because the production classifier was trained using prior productiondata, the production control system can in real time accurately andreliably detect problems and direct the taking of corrective action,forming a loop of receiving production data, analyzing production data,and correcting problems as needed in real time. Even if a problem cannotbe corrected in real time, the production system can signal thatcorrective action is needed or that the product is defective.

In some embodiments, the learning system generates a productionclassifier to label a certain type of data collected during anoccurrence of an activity. For example, the type of data may be video,and the activity may be welding. To generate the production classifier,the learning system receives example data collected from manyoccurrences of the activity and labels the example data for use astraining data when training the production classifier. For example, eachframe of the video may be assigned a weld characteristic as a label. Thelearning system then trains the production classifier using the trainingdata. The learning system may use any of a variety of machine learningtechniques such as a neural network, a support vector machine, adecision tree, a k-nearest neighbor algorithm, a linear regressionalgorithm, a relevance vector machine, ensembles of classifiers, and soon. When training is complete, the production classifier is ready to beput into production to, for example, label frames of a video.

The accuracy of the production classifier depends in large part on theamount of example data (i.e., number of observations) that is availableto use in training. If the amount of data is not sufficient, then theproduction classifier may suffer from overfitting in that the productiondata may accurately classify only data that is essentially identical tothe training data and cannot generalize to other data. Using priorsupervised learning techniques, a person needs to view each observationused in training and make a judgment call as to what is the mostaccurate label for that observation. For example, a person would need todecide whether the frame represents the formation or presence a perfectweld or a small glob. When the amount of data is large, the time neededto label the example data can be so time-consuming that the cost oflabeling the data is prohibitively expensive.

To label data in a cost-effective manner, the learning system uses asemi-supervised learning technique in which bootstrap data, representingmuch less data than the example data, is collected from a streamdifferent from that of the example data and is then labeled. A “stream”is a sequence of data from a sensor. The stream may include only onereading of the sensor (i.e., a sequence of one). For example, a videorepresents a chronological sequence of captured images from a camera,and a picture represents a single captured image from a camera, whichmay be the same camera used to capture the video. The bootstrap data maybe a picture (referred to as a snapshot) of each completed weld of theexample data. So each weld would have a picture as bootstrap data and avideo as example data. The bootstrap data and the example data arecollected via different data streams during manufacturing. For eachpicture of a weld to be labeled manually, the picture would be presentedto a person who would then mark the portions of the weld that havevarious weld characteristics. If the picture is of a horizontal weld andthe picture is 4096 pixels across, then a person may label pixels 0-791as being a break, pixels 792-857 as being a small glob, pixels 858-900as being a pit, pixels 901-2526 as being perfect, and so on. The videocamera may be mounted on the welding equipment and move, along with thewelder, relative to the surface being welded. Each frame of the videomay represent a small window of the most recent portion of the weld. Thevideo thus represents a moving window across the weld. If the weld is5.12 inches long, then the window may be 0.08 inches across. Because thepicture and video are correlated by space (e.g., the left-most portionof the picture represents the first few frames of the video), thelearning system can automatically label the frames of the video based onthis correlation. Continuing with the example, the learning system wouldlabel the first few frames as being a break, the next frames as being asmall glob, and so on. After the example data is labeled, the learningsystem trains the production classifier using the labeled example dataas training data.

In some situations, even the manual labeling of the bootstrap data canitself be relatively time-consuming especially when vast amounts ofexample data are needed. In some embodiments, the learning system mayemploy seed data that is manually labeled and train a bootstrapclassifier using the labeled seed data as training data. The learningsystem can then use the bootstrap classifier to label the large amountof bootstrap data automatically. The seed data may represent a subset ofthe example data or may be collected independently of the example data.For example, the seed data may be pictures of completed welds collectedfrom prior runs of the welding equipment and possibly even before thewelding equipment is equipped with the video camera. A person couldmanually label each portion of each picture of the seed data in a mannersimilar to that described above for labeling the bootstrap datamanually. After the bootstrap classifier is generated, the learningsystem can use the bootstrap classifier to label the bootstrap data,automatically label the example data using the labeled bootstrap data asdescribed above to generate the training data, and then train theproduction classifier using the training data.

In some embodiments, the adaptive system uses the production classifierthat is trained with bootstrap data, which labeled manually or with abootstrap classifier to provide labels for controlling production.During production, the adaptive system receives target data that is thesame type of data as the example data used to train the productionclassifier. Continuing with the example, the target data would be videotaken as the weld is created. As each frame of the video is received,the adaptive system uses the production classifier to label the frame.The adaptive system may provide the label to a corrective action systemthat decides whether any corrective action is needed (e.g., alter themanufacturing process in real time), and if so, what corrective actionis needed. For example, if the corrective action system receives labelsof “weakly sintered” for a sequence of frames, the corrective actionsystem may direct the production system to re-weld the correspondingportion of the weld. If, however, the sequence is short and bounded byframes with labels of “perfect,” the corrective action system may decideto take no corrective action. If the labels indicate a problem, but theweld is unrepairable, the corrective action system may direct theproduction system to abort the welding or abandon production of theproduct.

In some embodiments, the production control system can automaticallytrain a new production classifier in real time as a product is beingmanufactured when a bootstrap classifier is available. Duringmanufacturing, bootstrap data and example data can be extracted from newproduction data. The new production data may include new sensor datasuch as another video capturing a different angle or region of interestof the process, a thermal imaging pyrometer, a chemical detector, and soon. Based on various triggers, the production control system can directthe learning system to retrain the production classifier using the newbootstrap data and example data and possibly using some previouslycollected bootstrap and example data. The retrained productionclassifier may more accurately represent current production conditionsthat lead to a problem. For example, if the welder temperature was muchhigher during the current production run than in the production run usedto generate the training data for training the production classifier,the metal may solidify more slowly in the current production run. As aresult, the frames corresponding to, for example, weakly sintered weldsin the current production run may contain very different images fromthose used to train the production classifier. As such, the productionclassifier may not be able to detect all problems that could becorrected. By retraining the production classifier, the productionclassifier will be able to adapt to current production conditions. Theproduction classifier may be retrained based on various triggers. Theproduction classifier could be retrained on a periodic basis. Theproduction classifier may be retrained based on the amount of defectsfound in recently manufactured products. For example, the snapshot ofeach new weld can be labeled using the bootstrap classifier. If thelabeling of the snapshots indicates some threshold level ofacceptability has not been met, then the production control system candirect the learning system to retrain the production classifier. In someembodiments, the learning system may incrementally train the productionclassifier using various incremental machine learning techniques.

The production control system can be used with data other than or inaddition to image data (e.g., pictures and video) to controlmanufacturing. During manufacturing, sensors may be used to collect datathat includes various material properties such as coefficient offriction, electrical conductance, opacity, roughness, thickness, color,viscosity (e.g., material being deposited), and so on. Sensors may alsobe used to collect environmental conditions such as temperature, airpressure, air particulate count, gas concentrations, vibrations,electromagnetic waves, and so on. Sensors may also be used to collectequipment conditions such as nozzle temperature and flow rate, equipmentspeed, laser intensity, laser area, and so on. In addition, image dataneed not be based on visible light. For example, the bootstrap data maybe ultraviolet light, and a person may be presented with an image (i.e.,visible) showing the different frequencies of the ultraviolet light forlabeling. Non-image data can be used as bootstrap data and/or exampledata. For example, the bootstrap data can be roughness of a surface areathat is presented to a person for labeling as a map with differentshading to represent different levels of roughness, and thecorresponding example data can be a video. As another example, thebootstrap data can be pictures, and the example data can be viscosity ofmaterial being applied, nozzle temperature, and so on.

In some embodiments, the labeling of bootstrap data for use inautomatically labeling the corresponding example data can be used togenerate classifiers relating to fields other than manufacturing. Forexample, in oceanography, the bootstrap data can be images of areas ofthe ocean, and the example data can be wave height data collected atbuoys with wave height sensors. An oceanographer can label portions ofthe images based on risk to sailboats. The learning system canautomatically label corresponding wave height data to generate trainingdata and then train a sailboat risk classifier to assess the risk tosailboats automatically based on the wave height data. As anotherexample, in vehicle traffic flow, the bootstrap data can be images ofroadways taken periodically (e.g., every 30 seconds), and the exampledata can be speed and volume data for the vehicles, weather conditions,lighting conditions, on-ramp meter data, and so on. A traffic engineercan label the images based on level of additional flow that can beaccommodated (e.g., on a scale of 1-10). The learning system canautomatically label the corresponding example data and then train atraffic classifier to label data collected in real time based onadditional flow that can be accommodated and direct an on-ramp meteringsystem to adjust the vehicle flow accordingly. The learning system canbe used in many other fields such as meteorology, container processing(e.g., luggage scanning or container ship scanning), medical imaging,autonomous vehicle (e.g., drones, helicopters, unmanned aerial vehicles,planes) operation, threat detection, robots, satellite monitoring, andso on.

FIG. 2 is a diagram that illustrates an example relationship between asnapshot of a weld and a video of the welding process. A snapshot 210was taken after the welding was completed, and a video 220 was takenwhile welding. The snapshot includes d pixels in the horizontaldirection. A drawing 211 of the snapshot represents the weld. A snapshotportion 212 illustrates the start and the end of the snapshot thatcorresponds to a video portion 221 of the video. The snapshot portionstarts at pixel d/2 and ends at pixel d/2+e. The video includes d*100frames, and the video portion starts at frame d/2*100 and ends at(d/2+e)*100. When the video camera was positioned over the weldcorresponding to pixel d/2, it collected a frame 213 bounded on theright by pixel d/2 and extending W pixels to the left. Each framerepresents a moving window with a width of W pixels. As the video cameramoves (e.g., continuously) with the welder, it captures 100 frames perpixel of the snapshot.

FIG. 3 is a diagram that illustrates an example relationship between afriction map and height measurements of a manufacturing process. Afriction map 310 and height samplings 320 are analogous to the snapshotand video of FIG. 2. The friction map represents a measurement of thesurface friction of a rectangular area of an object after the processingof the surface is complete. The height samplings represent the heightwithin a window of the surface as a nozzle moves across the surfacedepositing a layer of material. The height within the window may becontinuously measured by a height measurement array. The depositedmaterial may flow after being deposited, so the height is subject tochange. The shading 311 represents variations in the height of thesurface. A friction map portion 312 illustrates the start and the end ofthe friction map that corresponds to a height sampling portion 321 ofthe height samplings. The surface is d units long. The friction mapportion starts at distance d/2 from the left end of the surface and endsat distance d/2+e. The height samplings include d*100 samples, and theheight sampling portion starts at sample d/2*100 and ends at sample(d/2+e)*100. When the height measurement array was positioned over thesurface at distance d/2, it collected a height sampling 313 bounded onthe right at distance d/2 and extending W units to the left. Each heightsampling represents a moving window with a width of W units. As theheight measurement device moves (e.g., continuously) with the nozzle, itcaptures 100 samplings per unit distance of the surface.

FIG. 4 is a block diagram that illustrates components of a productioncontrol system in some embodiments. The production control systemincludes a learning system 410, an adaptive system 420, equipment 430, aproduction data store 440, and a product specifications store 450. Thelearning system may include a generate bootstrap classifier component411 that includes a label seed data component 412 and a train bootstrapclassifier component 413 and may include a bootstrap classifierparameter store 414. The learning system includes a label bootstrap datacomponent 415, an automatically label example data component 416, and atrain production classifier component 417. The learning system alsoincludes a production classifier parameter store 418. The label seeddata component inputs seed data (e.g., from the production data store)and provides an interface through which a person can label the seeddata. The train bootstrap classifier component trains a bootstrapclassifier using the labeled seed data and stores the learned parametersof the classifier in the bootstrap classifier parameter store. The labelbootstrap data component receives bootstrap data (e.g., from theproduction data store) and either provides an interface through which aperson can label the bootstrap data or automatically labels thebootstrap data using the bootstrap classifier as represented by thebootstrap classifier parameters. The automatic label example datacomponent labels the example data based on its correlation to thelabeled bootstrap data. The train production classifier trains theproduction classifier using the labeled example data as training dataand stores the parameters of the production classifier in the productionclassifier parameter store.

The adaptive system includes a control production component 421, acollect production data component 422, an adapt production component423, and an analyze production data component 424. The controlproduction component retrieves product specification information fromthe product specifications store and generates control data to controlthe equipment in accordance with the product specifications. The collectproduction data component collects production data from the varioussensors associated with the equipment and stores the production data inthe production data store. The adapt production component receivesproduction data, invokes the analyze production data component todetermine whether corrective action is needed, and sends instructionsfor any necessary corrective action to the control production component.The analyze production component labels production data using theproduction classifier represented by the production classifierparameters and determines whether the label indicates that correctiveaction is needed.

The components of the production control system may execute on computingsystems that may include a central processing unit, input devices,output devices (e.g., display devices and speakers), storage devices(e.g., memory and disk drives), network interfaces, graphics processingunits, accelerometers, cellular radio link interfaces, globalpositioning system devices, and so on. The input devices may includekeyboards, pointing devices, touch screens, gesture recognition devices(e.g., for air gestures), head and eye tracking devices, microphones forvoice recognition, and so on. The computing systems may include desktopcomputers, laptops, tablets, e-readers, personal digital assistants,smartphones, gaming devices, servers, and so on. The learning system mayexecute on servers of a data center, massively parallel systems, and soon. The computing systems may access computer-readable media thatinclude computer-readable storage media and data transmission media. Thecomputer-readable storage media are tangible storage means that do notinclude a transitory, propagating signal. Examples of computer-readablestorage media include memory such as primary memory, cache memory, andsecondary memory (e.g., DVD) and other storage. The computer-readablestorage media may have recorded on it or may be encoded withcomputer-executable instructions or logic that implements components ofthe production control system. The data transmission media is used fortransmitting data via transitory, propagating signals or carrier waves(e.g., electromagnetism) via a wired or wireless connection. Thecomputing systems may include a secure cryptoprocessor as part of acentral processing unit for generating and securely storing keys and forencrypting and decrypting data using the keys.

The production control system may be described in the general context ofcomputer-executable instructions, such as program modules andcomponents, executed by one or more computers, processors, or otherdevices. Generally, program modules or components include routines,programs, objects, data structures, and so on that perform particulartasks or implement particular data types. Typically, the functionalityof the program modules may be combined or distributed as desired invarious examples. Aspects of the production control system may beimplemented in hardware using, for example, an application-specificintegrated circuit (ASIC).

FIG. 5 is a flow diagram that illustrates the processing of a generateproduction classifier component in some embodiments. A generateproduction classifier component 500 labels bootstrap data eitherautomatically or manually, labels the corresponding example data, andtrains the production classifier. In block 501, the component optionallyinvokes a generate bootstrap classifier component to generate abootstrap classifier using seed data. In block 502, the componentcollects bootstrap data and example data. In block 503, the componentlabels the bootstrap data. The component may label the bootstrap datausing a bootstrap classifier or may direct a person to manually labelthe bootstrap data. In block 504, the component automatically labels theexample data based on the labeled bootstrap data for use as trainingdata. In block 505, the component trains the production classifier usingthe training data, stores the parameters of the production classifier inthe production classifier parameter store, and then completes.

FIG. 6 is a flow diagram that illustrates the processing of a generatebootstrap classifier component in some embodiments. A generate bootstrapclassifier component 600 uses manually labeled seed data to train abootstrap classifier. In block 601, the component collects the seeddata. The seed data is of the same data type as the bootstrap data thatwill be used to automatically label the example data. The seed data maybe retrieved from a production data store of production data from priorproduction runs. In block 602, the component directs a person tomanually label the seed data for use as training data. In block 603, thecomponent trains the bootstrap classifier using the labeled seed data,stores the parameters of the bootstrap classifier in the bootstrapclassifier parameter store, and then completes.

FIG. 7 is a flow diagram that illustrates an adapt production componentin some embodiments. An adapt production component 700 loops receivingproduction data, analyzing the production data, and sending instructionsrelating to corrective actions that may be needed. In block 701, thecomponent receives the next production data from the productionequipment. In block 702, the component invokes an analyze productiondata component to analyze the production data to determine whethercorrective action is needed. In decision block 703, if the analysisindicates that corrective action is needed, then the component continuesat block 704, else the component continues at block 705. In block 704,the component sends corrective action instructions to the equipment andloops to block 701 to receive the next production data. In decisionblock 705, if the analysis indicates to abort the production, then thecomponent continues at block 706, else the component continues at block707. In block 706, the component sends abort instructions to theequipment and then completes. In decision block 707, if the productionis complete, then the component completes, else the component loops toblock 701 to receive the next production data.

FIG. 8 is a flow diagram that illustrates processing of an analyzeproduction data component in some embodiments. An analyze productiondata component 800 is passed production data and determines whethercorrective action is needed. In block 801, the component labels theproduction data using the production classifier as specified by theparameters in the production classifier parameter store. In decisionblock 802, if the label indicates that a correctable problem has beenencountered, then the component returns an indication that the problemis correctable, else the component continues at block 803. In decisionblock 803, if the label indicates that a problem is uncorrectable, thenthe component returns an indication to abort the production, else thecomponent returns an indication to continue the production.

Embodiment 1

A method performed by a computing device of generating a classifier tolabel data of a target data type, the data representing an activity, themethod comprising receiving bootstrap data and example data, thebootstrap data and the example data having a correlation in space ortime, the bootstrap data and the example data representing the activity,the example data being of the target data type; receiving from a user alabeling of the bootstrap data to generate labeled bootstrap data;automatically labeling the example data based on the correlation togenerate training data; and training the classifier based on thetraining data.

Embodiment 2

The method of embodiment 1 wherein the activity is a manufacturingprocess and the bootstrap data is snapshot data of the manufacturingprocess and the example data is video data of the manufacturing process.

Embodiment 3

The method of any combination of embodiments 1 through 2 furthercomprising applying the trained classifier to label newly collectedvideo data during the manufacturing process and providing instructionsto alter the manufacturing process in real time.

Embodiment 4

The method of any combination of embodiments 1 through 3 wherein a labelindicates whether a characteristic of a product is acceptable or notacceptable.

Embodiment 5

The method of any combination of embodiments 1 through 4 wherein labelsthat indicate that the characteristic is not acceptable include a labelindicating that manufacturing of the product should be aborted and alabel indicating that the characteristic is correctable during themanufacturing process.

Embodiment 6

The method of any combination of embodiments 1 through 5 wherein theactivity is a manufacturing process and the bootstrap data is from afirst stream of sensor data of the manufacturing process and the exampledata is from a second stream of sensor data of the manufacturingprocess.

Embodiment 7

The method of any combination of embodiments 1 through 6 furthercomprising applying the trained classifier to label newly collectedexample data during the manufacturing process and providing instructionsto alter the manufacturing process in real time.

Embodiment 8

The method of any combination of embodiments 1 through 7 wherein a labelindicates whether a characteristic of a product is acceptable or notacceptable.

Embodiment 9

The method of any combination of embodiments 1 through 8 wherein labelsthat indicate that the characteristic is not acceptable include a labelindicating that manufacturing of the product should be aborted and alabel indicating that the characteristic is correctable during themanufacturing process.

Embodiment 10

The method of any combination of embodiments 1 through 9 wherein theactivity is additive manufacturing.

Embodiment 11

The method of any combination of embodiments 1 through 9 wherein theactivity is in a field that is selected from a group consisting ofmanufacturing, oceanography, meteorology, traffic engineering, containerprocessing, medical imaging, autonomous vehicle operations, andsatellite monitoring.

Embodiment 12

The method of any combination of embodiments 1 through 11 wherein theclassifier is a neural network.

Embodiment 13

The method of embodiment any combination of embodiments 1 through 11wherein the classifier is selected from a group consisting of a supportvector machine, a decision tree, a k-nearest neighbor algorithm, alinear regression algorithm, a relevance vector machine, and ensemblesof classifiers.

Embodiment 14

A method performed by a computing device of generating a classifier tolabel data of a target data type, the method comprising: receiving seeddata having a seed data type; receiving from a user a labeling of theseed data to generate labeled seed data; training a bootstrap classifierto label data of the seed data type using the labeled seed data;receiving bootstrap data and example data, the bootstrap data andexample data representing different data streams, the bootstrap databeing of the seed data type and the example data being of the targetdata type; applying the bootstrap classifier to label the bootstrap datato generate training data; and training a production classifier to labeldata of the target data type using the training data.

Embodiment 15

The method of embodiment 14 wherein the seed data, the bootstrap data,and the example data are collected during different occurrences of sameactivity.

Embodiment 16

The method of any combination of embodiments 14 through 15 wherein thebootstrap data and the corresponding example data are collected duringthe same occurrence of an activity.

Embodiment 17

The method of any combination of embodiments 14 through 16 wherein thebootstrap data is snapshot data of a manufacturing process and theexample data is video data of the manufacturing process.

Embodiment 18

The method any combination of embodiments 14 through 17 furthercomprising applying the production classifier to label newly collectedvideo data during a manufacturing process and providing instructions toalter the manufacturing process in real time.

Embodiment 19

The method of any combination of embodiments 14 through 18 wherein alabel indicates whether a characteristic of a product is acceptable ornot acceptable.

Embodiment 20

The method of any combination of embodiments 14 through 19 whereinlabels that indicate that the characteristic is not acceptable include alabel indicating that manufacturing of the product should be abandonedand a label indicating that the characteristic is correctable during themanufacturing process.

Embodiment 21

The method of any combination of embodiments 14 through 20 wherein thebootstrap data is from a first stream of sensor data of the activity andthe example data is from a second stream of sensor data of the activity.

Embodiment 22

The method of any combination of embodiments 14 through 21 furthercomprising applying the production classifier to label newly collectedexample data during the activity and providing instructions to alter theactivity in real time.

Embodiment 23

The method of any combination of embodiments 14 through 12 wherein alabel indicates whether a characteristic of an output of the activity isacceptable or not acceptable.

Embodiment 24

The method of any combination of embodiments 14 through 23 whereinlabels that indicate that the characteristic is not acceptable include alabel indicating that the activity should be abandoned and a labelindicating that the characteristic is correctable during the activity.

Embodiment 25

The method of any combination of embodiments 14 through 24 wherein theseed data, the bootstrap data, and the example data are collected duringadditive manufacturing.

Embodiment 26

The method of any combination of embodiments 14 through 24 wherein thereceived data relates to a field that is selected from a groupconsisting of manufacturing, oceanography, meteorology, trafficengineering, container processing, medical imaging, autonomous vehicleoperations, and satellite monitoring.

Embodiment 27

The method of any combination of embodiments 14 through 26 wherein theclassifier is a neural network.

Embodiment 28

One or more computing devices for generating a classifier to control anadditive manufacturing process, a computing device comprising one ormore memories storing computer-executable instructions for controlling acomputing device, the instructions including a production classifier tolabel production data with a label, the production classifier beingtrained using labeled example data as training data, the labeled exampledata being automatically labeled based on labeled bootstrap data, thebootstrap data and the example data collected from sensors of anadditive manufacturing process, the example data being of a target datatype; and a component that receives production data from sensors duringan additive manufacturing process, the production data being of thetarget data type; applies the production classifier to label theproduction data; and controls in real time the additive manufacturingprocess based on the labels; and one or more processors for executingthe instructions stored in the one or more memories.

Embodiment 29

The one or more computing devices of embodiment 28 wherein a label ofthe production data indicates whether the product is acceptable and, ifit is not acceptable, whether a problem with the product is correctableor not.

Embodiment 30

The one or more computing devices of any combination of embodiments 28through 29 wherein the instructions further comprise instructionsincluding a bootstrap classifier to label bootstrap data, the bootstrapclassifier being trained using labeled seed data as training data, theseed data being collected from sensors of the additive manufacturingprocess; and a component to automatically label the bootstrap data usingthe bootstrap classifier.

Embodiment 31

The one or more computing devices of any combination of embodiments 28through 30 wherein the instructions further comprise instructionsincluding a component to retrain the production classifier usingbootstrap data and example data of current production data, thebootstrap data of the current production data being labeled using thebootstrap classifier.

Although the subject matter has been described in language specific tostructural features and/or acts, it is to be understood that the subjectmatter defined in the appended claims is not necessarily limited to thespecific features or acts described above. Rather, the specific featuresand acts described above are disclosed as example forms of implementingthe claims. Accordingly, the invention is not limited except as by theappended claims.

1. A method performed by a computing device of generating a classifierto label data of a target data type, the data representing an activity,the method comprising: receiving bootstrap data and example data, thebootstrap data and the example data having a correlation in space ortime, the bootstrap data and the example data representing the activity,the example data being of the target data type; receiving from a user alabeling of the bootstrap data to generate labeled bootstrap data;automatically labeling the example data based on the correlation togenerate training data; and training the classifier based on thetraining data.
 2. The method of claim 1 wherein the activity is amanufacturing process and the bootstrap data is snapshot data of themanufacturing process and the example data is video data of themanufacturing process.
 3. The method of claim 2 further comprisingapplying the trained classifier to label newly collected video dataduring the manufacturing process and providing instructions to alter themanufacturing process in real time.
 4. The method of claim 3 wherein alabel indicates whether a characteristic of a product is acceptable ornot acceptable.
 5. The method of claim 4 wherein labels that indicatethat the characteristic is not acceptable include a label indicatingthat manufacturing of the product should be aborted and a labelindicating that the characteristic is correctable during themanufacturing process.
 6. The method of claim 1 wherein the activity isa manufacturing process and the bootstrap data is from a first stream ofsensor data of the manufacturing process and the example data is from asecond stream of sensor data of the manufacturing process.
 7. The methodof claim 6 further comprising applying the trained classifier to labelnewly collected example data during the manufacturing process andproviding instructions to alter the manufacturing process in real time.8. The method of claim 7 wherein a label indicates whether acharacteristic of a product is acceptable or not acceptable.
 9. Themethod of claim 8 wherein labels that indicate that the characteristicis not acceptable include a label indicating that manufacturing of theproduct should be aborted and a label indicating that the characteristicis correctable during the manufacturing process.
 10. The method of claim1 wherein the activity is additive manufacturing.
 11. The method ofclaim 1 wherein the activity is in a field that is selected from a groupconsisting of manufacturing, oceanography, meteorology, trafficengineering, container processing, medical imaging, autonomous vehicleoperations, and satellite monitoring.
 12. The method of claim 1 whereinthe classifier is a neural network.
 13. The method of claim 1 whereinthe classifier is selected from a group consisting of a support vectormachine, a decision tree, a k-nearest neighbor algorithm, a linearregression algorithm, a relevance vector machine, and ensembles ofclassifiers.
 14. A method performed by a computing device of generatinga classifier to label data of a target data type, the method comprising:receiving seed data having a seed data type; receiving from a user alabeling of the seed data to generate labeled seed data; training abootstrap classifier to label data of the seed data type using thelabeled seed data; receiving bootstrap data and example data, thebootstrap data and example data representing different data streams, thebootstrap data being of the seed data type and the example data being ofthe target data type; applying the bootstrap classifier to label thebootstrap data to generate training data; and training a productionclassifier to label data of the target data type using the trainingdata.
 15. The method of claim 14 wherein the seed data, the bootstrapdata, and the example data are collected during different occurrences ofsame activity.
 16. The method of claim 14 wherein the bootstrap data andthe corresponding example data are collected during the same occurrenceof an activity.
 17. The method of claim 14 wherein the bootstrap data issnapshot data of a manufacturing process and the example data is videodata of the manufacturing process.
 18. The method of claim 15 furthercomprising applying the production classifier to label newly collectedvideo data during a manufacturing process and providing instructions toalter the manufacturing process in real time.
 19. The method of claim 18wherein a label indicates whether a characteristic of a product isacceptable or not acceptable.
 20. The method of claim 19 wherein labelsthat indicate that the characteristic is not acceptable include a labelindicating that manufacturing of the product should be abandoned and alabel indicating that the characteristic is correctable during themanufacturing process.
 21. The method of claim 14 wherein the bootstrapdata is from a first stream of sensor data of the activity and theexample data is from a second stream of sensor data of the activity. 22.The method of claim 21 further comprising applying the productionclassifier to label newly collected example data during the activity andproviding instructions to alter the activity in real time.
 23. Themethod of claim 22 wherein a label indicates whether a characteristic ofan output of the activity is acceptable or not acceptable.
 24. Themethod of claim 23 wherein labels that indicate that the characteristicis not acceptable include a label indicating that the activity should beabandoned and a label indicating that the characteristic is correctableduring the activity.
 25. The method of claim 14 wherein the seed data,the bootstrap data, and the example data are collected during additivemanufacturing.
 26. The method of claim 14 wherein the received datarelates to a field that is selected from a group consisting ofmanufacturing, oceanography, meteorology, traffic engineering, containerprocessing, medical imaging, autonomous vehicle operations, andsatellite monitoring.
 27. The method of claim 14 wherein the classifieris a neural network.
 28. One or more computing devices for generating aclassifier to control an additive manufacturing process, a computingdevice comprising: one or more memories storing computer-executableinstructions for controlling a computing device, the instructionsincluding: a production classifier to label production data with alabel, the production classifier being trained using labeled exampledata as training data, the labeled example data being automaticallylabeled based on labeled bootstrap data, the bootstrap data and theexample data collected from sensors of an additive manufacturingprocess, the example data being of a target data type; and a componentthat receives production data from sensors during an additivemanufacturing process, the production data being of the target datatype; applies the production classifier to label the production data;and controls in real time the additive manufacturing process based onthe labels; and one or more processors for executing the instructionsstored in the one or more memories.
 29. The one or more computingdevices of claim 28 wherein a label of the production data indicateswhether the product is acceptable and, if it is not acceptable, whethera problem with the product is correctable or not.
 30. The one or morecomputing devices of claim 28 wherein the instructions further compriseinstructions including: a bootstrap classifier to label bootstrap data,the bootstrap classifier being trained using labeled seed data astraining data, the seed data being collected from sensors of theadditive manufacturing process; and a component to automatically labelthe bootstrap data using the bootstrap classifier.
 31. The one or morecomputing devices of claim 30 wherein the instructions further compriseinstructions including a component to retrain the production classifierusing bootstrap data and example data of current production data, thebootstrap data of the current production data being labeled using thebootstrap classifier.